Symmetric Variants Of Logistic Smooth Transition Autoregressive Models: Monte Carlo Evidences,
2016
University of Ibadan, Nigeria
Symmetric Variants Of Logistic Smooth Transition Autoregressive Models: Monte Carlo Evidences, Olaoluwa S. Yaya, Olanrewaju I. Shittu
Journal of Modern Applied Statistical Methods
The Smooth Transition Autoregressive (STAR) models are becoming popular in modeling economic and financial time series. The asymmetric type of the model is the Logistic STAR (LSTAR) model, which is limited in its applications as a result of its asymmetric property, which makes it suitable for modelling specific macroeconomic time series. This study was designed to develop the Absolute Error LSTAR (AELSTAR) and Quadratic LSTAR (QLSTAR) models for improving symmetry and performance in terms of model fitness. Modified Teräsvirta’s Procedure (TP) and Escribano and Jordá's Procedure (EJP) were used to test for nonlinearity in the series. The performance of the …
Factorial Invariance Testing Under Different Levels Of Partial Loading Invariance Within A Multiple Group Confirmatory Factor Analysis Model,
2016
Washington State University
Factorial Invariance Testing Under Different Levels Of Partial Loading Invariance Within A Multiple Group Confirmatory Factor Analysis Model, Brian F. French, Holmes Finch
Journal of Modern Applied Statistical Methods
Scalar invariance in factor models is important for comparing latent means. Little work has focused on invariance testing for other model parameters under various conditions. This simulation study assesses how partial factorial invariance influences invariance testing for model parameters. Type I error inflation and parameter bias were observed.
Jmasm36: Nine Pseudo R^2 Indices For Binary Logistic Regression Models (Spss),
2016
Northern Illinois University
Jmasm36: Nine Pseudo R^2 Indices For Binary Logistic Regression Models (Spss), David A. Walker, Thomas J. Smith
Journal of Modern Applied Statistical Methods
This syntax program is an applied complement to Veall and Zimmermann (1994), Menard (2000), and Smith and McKenna (2013) and produces nine pseudo R2 indices, not readily accessible in statistical software such as SPSS, which are used to describe the results from binary logistic regression analyses.
A Spatial Analytical Framework For Examining Road Traffic Crashes,
2016
UNIVERSITY OF IBADAN
A Spatial Analytical Framework For Examining Road Traffic Crashes, Grace O. Korter
Journal of Modern Applied Statistical Methods
A number of different modeling techniques have been used to examine road traffic crashes for analytic and predictive purposes. Map-based spatial analysis is introduced. Applications are given which show the power in a combination of existing exploratory and statistical methods.
Jmasm38: Confidence Intervals For Kendall's Tau With Small Samples (Spss),
2016
Northern Illinois University
Jmasm38: Confidence Intervals For Kendall's Tau With Small Samples (Spss), David A. Walker
Journal of Modern Applied Statistical Methods
A syntax program, not readily expedient in statistical software such as SPSS, is provided for an application of confidence interval estimates with Kendall’s tau-b for small samples.
An Evaluation Of Pareto, Lognormal And Pps Distributions: The Size Distribution Of Cities In Kerala, India,
2016
S.R.M. Medical College and Research Centre, Kattankulathur, India
An Evaluation Of Pareto, Lognormal And Pps Distributions: The Size Distribution Of Cities In Kerala, India, Christopher A. Vallabados, Subbarayan A. Arumugam
Journal of Modern Applied Statistical Methods
The Pareto-Positive Stable (PPS) distribution is introduced as a new model for describing city size data of a region in a country. The PPS distribution provides a flexible model for fitting the entire range of a set of city size data and the classical Pareto and Zipf distributions are included as a particular case.
Determination Of Optimal Tightened Normal Tightened Plan Using A Genetic Algorithm,
2016
University of Madras
Determination Of Optimal Tightened Normal Tightened Plan Using A Genetic Algorithm, Sampath Sundaram, Deepa S. Parthasarathy
Journal of Modern Applied Statistical Methods
Designing a tightened normal tightened sampling plan requires sample sizes and acceptance number with switching criterion. An evolutionary algorithm, the genetic algorithm, is designed to identify optimal sample sizes and acceptance number of a tightened normal tightened sampling plan for a specified consumer’s risk, producer’s risk, and switching criterion. Optimal sample sizes and acceptance number are obtained by implementing the genetic algorithm. Tables are reported for various choices of switching criterion, consumer’s quality level, and producer’s quality level.
Model-Based Outlier Detection System With Statistical Preprocessing,
2016
Anna University, Tiruchirappalli, India
Model-Based Outlier Detection System With Statistical Preprocessing, D. Asir Antony Gnana Singh, E. Jebalamar Leavline
Journal of Modern Applied Statistical Methods
Reliability, lack of error, and security are important improvements to quality of service. Outlier detection is a process of detecting the erroneous parts or abnormal objects in defined populations, and can contribute to secured and error-free services. Outlier detection approaches can be categorized into four types: statistic-based, unsupervised, supervised, and semi-supervised. A model-based outlier detection system with statistical preprocessing is proposed, taking advantage of the statistical approach to preprocess training data and using unsupervised learning to construct the model. The robustness of the proposed system is evaluated using the performance evaluation metrics sum of squared error (SSE) and time to …
Analyzing Different Sampling Designs (Sas),
2016
Educational Testing Service
Analyzing Different Sampling Designs (Sas), Ying Lu
Journal of Modern Applied Statistical Methods
Various sampling designs are reviewed within the framework of probability sampling. SAS® code to estimate means and proportions, and their standard errors, using different sampling designs are illustrated using example data sets.
Bayesian Estimation Of P[Y < X] Based On Record Values From The Lomax Distribution And Mcmc Technique,
2016
Al-Azhar University, Cairo, Egypt
Bayesian Estimation Of P[Y < X] Based On Record Values From The Lomax Distribution And Mcmc Technique, Mohamed A. W Mahmoud, Rashad M. El-Sagheer, Ahmed A. Soliman, Ahmed H. Abd Ellah
Journal of Modern Applied Statistical Methods
Our interest is in estimating the stress-strength reliability R = P[Y < X], where X and Y follow the Lomax distribution with common scale parameter. We discuss the problem in the situation where the stress measurements and the strength measurements are both in terms of records. Firstly, we obtain the MLE of R in general case (the common scale parameter is unknown). The MLE of the three unknown parameters can be obtained by solving one non-linear equation. We provide a simple fixed point type algorithm to find the MLE. We propose percentile bootstrap confidence intervals of R. A Bayes …
Graphing Effects As Fuzzy Numbers In Meta-Analysis,
2016
Florida State University
Graphing Effects As Fuzzy Numbers In Meta-Analysis, Christopher G. Thompson
Journal of Modern Applied Statistical Methods
Prior to quantitative analyses, meta-analysts often explore descriptive characteristics of effect sizes. A graphic is proposed that treats effect sizes as fuzzy numbers. This plot can provide meta-analysts with such information such as heterogeneity of effects, precision of estimates, possible clusters, and existence of outliers.
Principal Component Preliminary Test Estimator In The Linear Regression Model,
2016
Department of Mathematics and Statistics, University of Jaffna, Sri Lanka
Principal Component Preliminary Test Estimator In The Linear Regression Model, Sivarajah Arumairajan, Pushpakanthie Wijekoon
Journal of Modern Applied Statistical Methods
A Preliminary Test Estimator is introduced based on Principal Component Regression Estimator defined in the linear regression model when the stochastic restrictions are available in addition to the sample information, and when the explanatory variables are multicollinear. It is further developed as a large sample preliminary test estimator by using Wald (WA), Likelihood Ratio (LR), and Lagrangian Multiplier (LM) tests. Stochastic properties of this estimator based on F test as well as WA, LR, and LM tests are derived, and the performance of the estimator is compared using WA, LR, and LM tests with respect to Mean Square Error Matrix …
New Procedures Of Estimating Proportion And Sensitivity Using Randomized Response In A Dichotomous Finite Population,
2016
School of Studies in Statistics, Vikram University Ujjain - M.P. - India.
New Procedures Of Estimating Proportion And Sensitivity Using Randomized Response In A Dichotomous Finite Population, Tanveer A. Tarray, Housila P. Singh
Journal of Modern Applied Statistical Methods
The problem of estimating the population proportion possessing a sensitive attribute using simple random sampling with replacement (SRSWR) is advocated. Two new procedures are proposed. The suggested models are more efficient than the Huang (2004) randomized response technique under some realistic conditions. Numerical and graphic illustrations are given.
Construction Of Pair-Wise Balanced Design,
2016
Manonmaniam Sundaranar University
Construction Of Pair-Wise Balanced Design, Rajarathinam Arunachalam, Mahalakshmi Sivasubramanian, Dilip Kumar Ghosh
Journal of Modern Applied Statistical Methods
A new procedure for construction of pair wise balanced design with equal replication and un-equal block sizes based on factorial design have been evolved. Numerical illustration also provided. It was found that the constructed pair wise balanced design was found to be universal optimal.
Propensity Score Methods : A Simulation And Case Study Involving Breast Cancer Patients.,
2016
University of Louisville
Propensity Score Methods : A Simulation And Case Study Involving Breast Cancer Patients., John Craycroft
Electronic Theses and Dissertations
Observational data presents unique challenges for analysis that are not encountered with experimental data resulting from carefully designed randomized controlled trials. Selection bias and unbalanced treatment assignments can obscure estimations of treatment effects, making the process of causal inference from observational data highly problematic. In 1983, Paul Rosenbaum and Donald Rubin formalized an approach for analyzing observational data that adjusts treatment effect estimates for the set of non-treatment variables that are measured at baseline. The propensity score is the conditional probability of assignment to a treatment group given the covariates. Using this score, one may balance the covariates across treatment …
Generalized Linear Model Analyses For Treatment Group Equality When Data Are Non-Normal,
2016
University of Manitoba
Generalized Linear Model Analyses For Treatment Group Equality When Data Are Non-Normal, Harvey J. Kesleman, Abdul R. Othman, Rand R. Wilcox
Journal of Modern Applied Statistical Methods
One of the validity conditions of classical test statistics (e.g., Student’s t-test, the ANOVA and MANOVA F-tests) is that data be normally distributed in the populations. When this and/or other derivational assumptions do not hold the classical test statistic can be prone to too many Type I errors (i.e., falsely rejecting too often) and/or have low power (i.e., failing to reject when the null hypothesis is false) to detect treatment effects when they are present. However, alternative procedures are available for assessing equality of treatment group effects when data are non-normal. For example, researchers can use robust estimators …
Liu-Type Logistic Estimators With Optimal Shrinkage Parameter,
2016
Necmettin Erbakan University
Liu-Type Logistic Estimators With Optimal Shrinkage Parameter, Yasin Asar
Journal of Modern Applied Statistical Methods
Multicollinearity in logistic regression affects the variance of the maximum likelihood estimator negatively. In this study, Liu-type estimators are used to reduce the variance and overcome the multicollinearity by applying some existing ridge regression estimators to the case of logistic regression model. A Monte Carlo simulation is given to evaluate the performances of these estimators when the optimal shrinkage parameter is used in the Liu-type estimators, along with an application of real case data.
The Xgamma Distribution: Statistical Properties And Application,
2016
Malabar Cancer Centre, Thalassery
The Xgamma Distribution: Statistical Properties And Application, Subhradev Sen, Sudhansu S. Maiti, N. Chandra
Journal of Modern Applied Statistical Methods
A new probability distribution, the xgamma distribution, is proposed and studied. The distribution is generated as a special finite mixture of exponential and gamma distributions and hence the name proposed. Various mathematical, structural, and survival properties of the xgamma distribution are derived, and it is found that in many cases the xgamma has more flexibility than the exponential distribution. To evaluate the comparative behavior, stochastic ordering of the distribution is studied. To estimate the model parameter, the method of moment and the method of maximum likelihood estimation are proposed. A simulation algorithm to generate random samples from the xgamma distribution …
Analysis And Modeling Of Statistical Properties Of Fmdfb Subband Coefficients,
2016
Anna University, Tiruchirappalli, India
Analysis And Modeling Of Statistical Properties Of Fmdfb Subband Coefficients, E. Jebamalar Leavline, Sutha Shunmugam
Journal of Modern Applied Statistical Methods
Fast Multiscale Directional Filter Bank (FMDFB) is an image representation scheme used in several image processing applications. The statistical nature of the FMDFB subbands is analyzed, and a mathematical model of FMDFB coefficients is proposed. Experimental results are justified by goodness-of-fit tests.
Jmasm37: Simple Response Surface Methodology Using Rsreg (Sas),
2016
University Science Malaysia
Jmasm37: Simple Response Surface Methodology Using Rsreg (Sas), Wan Muhamad Amir, Mohamad Shafiq, Kasypi Mokhtar, Nor Azlida Aleng, Hanafi A.Rahim, Zalila Ali
Journal of Modern Applied Statistical Methods
Response surface methodology (RSM) can be used when the response variable, y, is influenced by several variables, x’s. When treatments take the form of quantitative values, then the true relationship between response variables and independent variables might be known. Examples are given in SAS.
